Sourcefire Becomes First Security Vendor to Integrate Intel QuickAssist Pattern Matching Technology
Network security services provider Sourcefire has announced the integration of Intel’s QuickAssist Pattern Matching technology with the new version of its Snort network intrusion detection system.
The company, which was founded by Snort developer Martin Roesch, informed that Intel’s technology will replace Snort’s own pattern matching technology in the beta version of Snort 2.9, in order to offer more sophisticated and deeper network inspection. 
The Intel QuickAssist Pattern Matching technology will allow the company to expand Snort’s network protection functionality and enhancing its performance and speed.
Snort is an open source network intrusion detection tool which allows users to monitor their whole network from a single platform. The security tool is capable of scanning network traffic in real-time and providing a detailed reports on network traffic.
Snort is also used for packet logging on Internet Protocol networks and adds content searching, detailed analysis of protocol and pattern matching in massive content databases to its long list of capabilities. However, the security system is most apt in detecting secret probes and attacks on a system and protects the network against fingerprinting attempts on operating systems, buffer overflows and common gateway interface.
As enterprises are updating their existing IT infrastructures to faster and efficient network speeds, the integration of Intel’s pattern matching technology will allow Snort to offer scalable and cost effective security services at parallel speeds.
In the light of recent cyber attacks on US based organizations originating from China and other countries of the world, organizations are investing heavily in securing their internal systems against external and internal attacks.
The Intel QuickAssist pattern matching API will allow Snort to offer deeper packet sniffing capabilities which will compliment its intrusion detection and prevention systems.
